There are many products and services where actions are conditioned on an aggregate total of events. For example, special offers and bonuses may be given based on an accumulated amount spent. Likewise, the rank of an item in a given hierarchy may be based on its relative popularity as measured by a number of visitors or purchasers and the like. When a desired action pertains to only one specific customer, for example a frequent flyer eligible for free flight tickets, it is straightforward to define a database structure where cumulative ticket price is stored for each member of the frequent flyers' club so as to allow bonuses to be awarded when a member's cumulative ticket price exceeds a predetermined threshold.
It is more difficult to aggregate scores when the scores themselves are aggregates to which more than one item or user contributes. For example, US2003115586 (Lejouan et al.) discloses a method for measuring and analyzing activity of multiple terminals on a data communications network. Data exchanged over the communications network by the terminals is stored in a database for subsequent analysis. Thus data comprising at least descriptive data of each packet transmitted and received by each terminal, is collected and transmitted to a central server where aggregate databases are generated to supply data representing the activity of a panel of equipment items connected to the communications network. This allows actions to be taken based on aggregate behavior of a target audience.
In a typical Internet scenario, an advertisement is displayed on a number of different websites so that users who enter some or all of these websites are repeatedly exposed to that advertisement. In order to determine a single website that serves as the most effective vehicle for disseminating the advertisement, it is only necessary to survey and count the number of users surfing each website.
WO0133831 (Peroffet al) discloses an interactive web-based survey instrument and method that allows the application of different selection criteria to be applied at a subscriber website as a survey participant. Selection criteria may be applied at a subscriber website that is visited, such as random or periodic selection of visiting users. Moreover, the user's activities at the subscriber site may be monitored, such as number of web pages viewed, time spent viewing and so forth.
US2002082901 (Doron et al.) discloses a system and method for discovering relationships among items and for recommending items based on the discovered relationships. The recommendations are based on user profiles that take into account actual preferences of users, without requiring users to complete questionnaires.
US2005021440 (Dresden) provides a virtual system that assists in the procurement of advertising on an Internet vendor site for the sale of products or services. The system links to a user's financial package to get data on the products or services and allows the user to set financial parameters based on the desired financial goals related to the product and advertising. Performance data regarding advertising is accessed and financial rules generated which are applied to generate a target price for advertising or one or more products. The system can acquire advertising automatically or assist in the submission of bids in an auction of advertising.
But frequently advertisers run campaigns where an advertisement is accessible from multiple websites and they want to know which combination of websites is the most effective vehicle for disseminating the advertisement. This information cannot be derived merely from the cumulative numbers of users surfing each website since frequently the same user will see the advertisement several times on different websites. So what really interests the advertiser is whether some of the websites are practically redundant such that not displaying the advertisement on one or more websites will not noticeably detract from the total number of unique surfers who are exposed to the advertisement.
To determine this it is not sufficient merely to count the number of exposures to each advertisement. It is necessary to identify each user surfing all websites that display the advertisement, to determine for each website of interest a list of unique surfers and to use this information to determine the most cost-effective combination of websites whereby maximum exposure is achieved for the least cost. Presently, collection of this data is implemented in the form of log lists with every log specifying “user x; visited site y”. Such log lists require extensive processing to draw meaningful conclusions, thus requiring significant computation and computer resources such as memory and CPU time.
It would therefore be desirable to provide a simplified mechanism that allows the number of visitors to each website to be tracked in such a manner as to allow fast determination of the most effective combination of websites to produce a more cost-effective exposure of the campaign.